Presenter: Oyekanmi Nash, PhD
Node Principal Investigator,
H3Africa Bioinformatics Network Node at
National Biotechnology ...
NIH/WT H3Africa Research Network
H3Africa/HVP: Leveraging Potentials
• NIH/WT-H3Africa
• Collaborative Centers : 8
• Research Projects: 9
• BioBanks : 4
• ...
Map of Africa showing the distribution
of nodes in the H3ABioNet network
H3Africa: Bioinformatics Network
• H3ABioNet: a sustainable African
Bioinformatics Network for H3Africa
The network provid...
ORGANIZATION OF THE HVP Nigeria Node
ICCAC Country Representative : Prof. Oyekanmi Nash,
Alternate Representative: Hadiza ...
ORGANIZATION OF THE HVP NIGERIA NODE II
The staff members of the Node include:
• Alternate Representative - Hadiza Rasheed...
Background – Cardiometabolic Diseases
• Worldwide cardiometabolic diseases are the major causes of:
• Disability; Rising H...
A Strategy in Africa to Address Burden of
Cardiometabolic Diseases
• Genomic and Environmental Determinants
(H3Africa Proj...
Examples of Projected Massive and Complex
Datasets from H3Africa Projects (2013….
Type 2 Diabetes Project
• 12,000 Cases a...
DATA SCIENCE
• Data Flow
• Data Curation
• Data Analysis
“The major bottleneck in genome sequencing is
no longer data generation—the computational
challenges around data analysis,...
Visual Discovery Tools
Visual Discovery Tasks
• Exploration
• Mining
• Analysis
To access and analyze data visually at the...
What is Visual Analytics?
http://www.slideshare.net/TableauSoftware/visual-analytics-best-practices
“Visual analytics is t...
Knowledge-Building Insights from
Visual Analytics
http://www.flickr.com/photos/pnnl/6310387725/
Visual Interfaces
Examples of Visual Analytics Software
http://www.vacommunity.org/Education+Resources
Toolkits
Analytic Tools
Jigsaw
Types of Visual Discovery Tools
H3ABioNet Workshop: Visual Analytics of
Human Genomics Variation Datasets
July 2013
Opportunities exist for African resear...
Long-Term Goal of Project
• Visual Analytical System for
• discovery of molecular consequences of variants and linked
tran...
Research Approach
 Obtain Datasets
 Ensembl Genome Browser (www.ensembl.org)
 BioMart for genes and variants
 Database...
Use Case – Gene Families
 AQUAPORIN – Water and glycerol transporter
 13 Mammalian Aquaporins (AQP0-AQP12).
 Malfunctio...
Molecular Consequences of Single Nucleotide Variants
of Aquaporin Genes - Overview
Visual Analytical System for Screening Disease Linked Gene Variants
Integrates data from ENSEMBL and Database of Alternate...
Identification of variants that could affect transcript expression in adipose tissues
Summary
In Africa, researchers will be able to use visual discovery tools to make
DISCOVERIES from large-scale molecular a...
Acknowledgments
• H3Africa Bioinformatics Network (H3ABioNet)
– National Human Genome Research Institute
– NIH Common Fund...
Visual Analytical Screening System for  Disease Linked Gene Variants - Oyekanmi Nash
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Visual Analytical Screening System for Disease Linked Gene Variants - Oyekanmi Nash

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Background: The major bottleneck in genome sequencing is no longer data generation, but the computational challenges around data analysis, display and integration. New approaches and methods are, therefore, required to meet these challenges. Visual analytics is the representation and presentation of data that exploits human visual perception abilities in order to amplify cognition. Opportunities exist for African researchers to expand the use of visual discovery tools and curated datasets to enable visual discovery (exploration, mining and analysis via interactive visual interfaces) of bioinformatics results from high-quality genomics research.
Methods: We are developing a system of visual analytics resources that are based on molecular and clinical data including molecular consequences of single nucleotide variants; the RNA-seq expression levels of transcripts; and the functional sites in protein sequences.
Results: We have developed an initial set of visual analytics resources with the use case as the major intrinsic protein family of water and glycerol transporters. Members of these protein family have been implicated in diverse cardiometabolic diseases. The computational resources developed can be adapted for gene lists including those obtained from high-throughput assays. The long-term goal of the project is to empower researchers to make discoveries from largescale molecular and clinical datasets to support decision-making on genetic and environmental determinants of cardiometabolic diseases in Africa.

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  • Visual Analytical Screening System for Disease Linked Gene Variants - Oyekanmi Nash

    1. 1. Presenter: Oyekanmi Nash, PhD Node Principal Investigator, H3Africa Bioinformatics Network Node at National Biotechnology Development Agency (NABDA) Abuja, Nigeria Visual Analytical Screening System for Disease Linked Gene Variants Visual Discovery Tools Cardiometabolic Diseases
    2. 2. NIH/WT H3Africa Research Network
    3. 3. H3Africa/HVP: Leveraging Potentials • NIH/WT-H3Africa • Collaborative Centers : 8 • Research Projects: 9 • BioBanks : 4 • Bioinformatics Network : 1
    4. 4. Map of Africa showing the distribution of nodes in the H3ABioNet network
    5. 5. H3Africa: Bioinformatics Network • H3ABioNet: a sustainable African Bioinformatics Network for H3Africa The network provide: • computational infrastructure and hardware, • human resources, • tools and computational solutions for genomic and population-based research, and • communications among African researchers and other interested parties. These aims are be achieved by: • providing user support, • training and capacity development, • research and tools development, and • outreach and communication.
    6. 6. ORGANIZATION OF THE HVP Nigeria Node ICCAC Country Representative : Prof. Oyekanmi Nash, Alternate Representative: Hadiza Rasheed-Jada Reports directly to the DG/CEO, NABDA/FMST
    7. 7. ORGANIZATION OF THE HVP NIGERIA NODE II The staff members of the Node include: • Alternate Representative - Hadiza Rasheed-Jada • Node Manager - Atinuke Hassan • Systems Administrator - Adekunle Farouk • Research Associates - Abimbola Kashim - Deborah Fasesan - Taoheed Abdulkareem - Ayodele Fakoya - Adijat Ozohu Jimoh • Post-doctoral Researcher - Dr. Segun Fatumo
    8. 8. Background – Cardiometabolic Diseases • Worldwide cardiometabolic diseases are the major causes of: • Disability; Rising Healthcare Costs and Deaths • Examples: • Type 2 diabetes, hypertension, dyslipidemia, coronary heart disease and chronic kidney disease • Over the next 7 years • Africa is projected to experience the largest increase in death rates from cardiovascular disease, cancer, respiratory disease and diabetes (Aikins et al., 2010) Noncommunicable Diseases AFR - 2015 AFR - 2030 Fold Change Diabetes mellitus 205,378.79 390,614.91 1.90 Malignant neoplasms 521,029.65 966,876.53 1.86 Other neoplasms 20,155.67 37,375.03 1.85 Cardiovascular diseases 1,179,320.20 1,966,212.66 1.67 Respiratory diseases 234,649.72 356,651.78 1.52 Source: Global Health Estimates (GHE) 2013: Deaths by age, sex and cause
    9. 9. A Strategy in Africa to Address Burden of Cardiometabolic Diseases • Genomic and Environmental Determinants (H3Africa Projects) • H3Africa Kidney Disease Research Network • Genomic and environmental risk factors for cardiometabolic disease in Africans • Burden, spectrum and etiology of type 2 diabetes in sub-Saharan Africa • …..
    10. 10. Examples of Projected Massive and Complex Datasets from H3Africa Projects (2013…. Type 2 Diabetes Project • 12,000 Cases and 12,000 Controls • Sequencing of known T2DM regions • Genome-wide genotyping arrays • Whole exome/genome sequencing Body Composition Project • African genome structure • Phenotyping and sampling for Cohorts • Genetic and environmental contribution to body composition (~12,000 individuals) These research investigations rely significantly on bioinformatics analysis and inferences from large and heterogeneous datasets obtained from populations inside and outside Africa.
    11. 11. DATA SCIENCE • Data Flow • Data Curation • Data Analysis
    12. 12. “The major bottleneck in genome sequencing is no longer data generation—the computational challenges around data analysis, display and integration are now rate limiting. New approaches and methods are required to meet these challenges”. National Human Genome Research Institute Strategic Plan: Charting a course for genomic medicine from base pairs to bedside http://www.genome.gov/Pages/About/Planning/2011NHGRIStrategicPlan.pdf Making Discoveries from the Massive and Complex Genomics Datasets and Bioinformatics Results from H3Africa Projects
    13. 13. Visual Discovery Tools Visual Discovery Tasks • Exploration • Mining • Analysis To access and analyze data visually at the speed of thought with minimal or no IT assistance and then share the results of their discoveries with colleagues, usually in the form of an interactive dashboard Benefits • Data sharing • Collaboration • Easy to Deploy • Research in Limited or No Internet Access
    14. 14. What is Visual Analytics? http://www.slideshare.net/TableauSoftware/visual-analytics-best-practices “Visual analytics is the representation and presentation of data that exploits our visual perception abilities in order to amplify cognition.” - Andy Kirk, author of “Data Visualization: a successful design process”
    15. 15. Knowledge-Building Insights from Visual Analytics http://www.flickr.com/photos/pnnl/6310387725/
    16. 16. Visual Interfaces
    17. 17. Examples of Visual Analytics Software http://www.vacommunity.org/Education+Resources Toolkits Analytic Tools Jigsaw
    18. 18. Types of Visual Discovery Tools
    19. 19. H3ABioNet Workshop: Visual Analytics of Human Genomics Variation Datasets July 2013 Opportunities exist for African researchers to expand the use of visual discovery tools and curated datasets to enable visual discovery (exploration, mining and analysis via interactive visual interfaces) of bioinformatics results from high-quality genomics research.
    20. 20. Long-Term Goal of Project • Visual Analytical System for • discovery of molecular consequences of variants and linked transcript expression for sets of genes or gene families http://www.ensembl.org/info/genome/variation/predicted_data.html Molecular Consequences of Gene Variants Transcripts
    21. 21. Research Approach  Obtain Datasets  Ensembl Genome Browser (www.ensembl.org)  BioMart for genes and variants  Database of Alternate Transcript Expression  Data Download for transcript expression values  Data Cleaning and Preparation  Scripting and Spreadsheets  Construct Views and Dashboards  To address scientific questions such as:  Identify molecular consequences of gene variants (Single Nucleotide Variants) in specific disease or trait.  Identify gene variants that result in multiple molecular consequences in gene transcripts.  Identify gene variant specific for transcript  Compare RNA-Seq expression values for gene transcripts in tissues.
    22. 22. Use Case – Gene Families  AQUAPORIN – Water and glycerol transporter  13 Mammalian Aquaporins (AQP0-AQP12).  Malfunction or absence linked to disease.  Adipose AQP7 deficiency is associated with an increase of intracellular glycerol content.  Up-regulation of AQP1 in the glomeruli of most diseased kidneys. Reference: Hibuse et al. (2005). Aquaporin 7 deficiency is associated with development of obesity through activation of adipose glycerol kinase. Proc Natl Acad Sci U S A. 2005 Aug 2;102(31):10993-8. http://www.ncbi.nlm.nih.gov/pubmed/16009937
    23. 23. Molecular Consequences of Single Nucleotide Variants of Aquaporin Genes - Overview
    24. 24. Visual Analytical System for Screening Disease Linked Gene Variants Integrates data from ENSEMBL and Database of Alternate Transcript Expression (DBATE) DataSources Blending of Data Dimensions from multiple Data Sources Identifies Variants linked to Transcripts Insights: rs199936776 is unique to AQP7-004 and could affect expression of transcript or properties of protein isoform
    25. 25. Identification of variants that could affect transcript expression in adipose tissues
    26. 26. Summary In Africa, researchers will be able to use visual discovery tools to make DISCOVERIES from large-scale molecular and clinical datasets to support decision-making on genetic and environmental determinants of cardiometabolic diseases. Visual Analytics can facilitate collaboration between Data Experts and Subject Matter Experts
    27. 27. Acknowledgments • H3Africa Bioinformatics Network (H3ABioNet) – National Human Genome Research Institute – NIH Common Fund – Grant U41HG006941 • National Institutes of Health • Dr. Raphael Isokpehi, Bethune-Cookman University, Florida, USA • National Biotechnology Development Agency, Federal Ministry of Science and Technology, Nigeria • Visual Analytics in Biology Curriculum Network

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